Deconvolute individual genomes from metagenome sequences through short read clustering

Kexue Li, Yakang Lu, Li Deng*, Lili Wang, Lizhen Shi, Zhong Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality.

Original languageEnglish (US)
Article number8966
Issue number4
StatePublished - 2020


  • Apache Spark
  • Metagenome clustering
  • Short-read clustering

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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